Risk-Seeking versus Risk-Avoiding Investments in Noisy Periodic Environments

We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, $x(t)$, and at each time step invest a particular fraction, $q(t)$, of their budget. The return on investment (RoI), $r(t)$, is characterized by a periodic function with different types and levels of noise. Risk-avoiding agents choose their fraction $q(t)$ proportional to the expected positive RoI, while risk-seeking agents always choose a maximum value $q_{max}$ if they predict the RoI to be positive ("everything on red"). In addition to these different strategies, agents have different capabilities to predict the future $r(t)$, dependent on their internal complexity. Here, we compare 'zero-intelligent' agents using technical analysis (such as moving least squares) with agents using reinforcement learning or genetic algorithms to predict $r(t)$. The performance of agents is measured by their average budget growth after a certain number of time steps. We present results of extensive computer simulations, which show that, for our given artificial environment, (i) the risk-seeking strategy outperforms the risk-avoiding one, and (ii) the genetic algorithm was able to find this optimal strategy itself, and thus outperforms other prediction approaches considered.Comment: 27 pp. v2 with minor corrections. See http://www.sg.ethz.ch for more inf

The MSSM prediction for W+/- H-/+ production by gluon fusion

We discuss the associated W+/- H-/+ production in p p collision for the Large Hadron Collider. A complete one-loop calculation of the loop-induced subprocess g g -> W+/- H-/+ is presented in the framework of the Minimal Supersymmetric Standard Model (MSSM), and the possible enhancement of the hadronic cross section is investigated under the constraint from the squark direct-search results and the low-energy precision data. Because of the large destructive interplay in the quark-loop contributions between triangle-type and box-type diagrams, the squark-loop contributions turn out to be comparable with the quark-loop ones. In particular, the hadronic cross section via gluon fusion can be extensively enhanced by squark-pair threshold effects in the box-type diagrams, so that it can be as large as the hadronic cross section via the b b-bar -> W+/- H-/+ subprocess which appears at tree level.Comment: 35 pages, 7 figures, version to appear in Physical Review

Squark Loop Correction to W^{+-} H^{-+} Associated Hadroproduction

We study the squark loop correction to W^{+-} H^{-+} associated hadroproduction via gluon-gluon fusion within the minimal supersymmetric extension of the standard model. We list full analytic results and quantitatively analyze the resulting shift in the cross section at the CERN Large Hadron Collider assuming a supergravity-inspired scenario.Comment: 13 pages (Latex), 5 figures (Postscript

A new AXT format for an efficient SpMV product using AVX-512 instructions and CUDA

The Sparse Matrix-Vector (SpMV) product is a key operation used in many scientific applications. This work proposes a new sparse matrix storage scheme, the AXT format, that improves the SpMV performance on vector capability platforms. AXT can be adapted to different platforms, improving the storage efficiency for matrices with different sparsity patterns. Intel AVX-512 instructions and CUDA are used to optimise the performances of the four different AXT subvariants. Performance comparisons are made with the Compressed Sparse Row (CSR) and AXC formats on an Intel Xeon Gold 6148 processor and an NVIDIA Tesla V100 Graphics Processing Units using 26 matrices. On the Intel platform the overall AXT performance is 18% and 44.3% higher than the AXC and CSR respectively, reaching speed-up factors of up to x7.33. On the NVIDIA platform the AXT performance is 44% and 8% higher than the AXC and CSR performances respectively, reaching speed-up factors of up to x378.5S